TinghuiWang / pyActLearn

        if cur_truth != prev_truth:

            truth_time = time_list[i]

            # Check the start boundary

            if truth[i] == prediction[i]:

                # If current prediction is correct, then it can only be overfill of current truth label.

                j = i - 1

                while j >= 0 and prediction_scoring[j] == 'O':

                    j -= 1

                # If there is no overfill for cur_truth, and the current truth and prediction are the same,

                # then there is no start_boundary mismatch.

                start_mismatch[cur_truth].append((time_list[j + 1] - truth_time).total_seconds())

            else:

                # If current prediction is incorrect, then it can only be underfill of current truth label at start

                # boundary.

                j = i

                while j < truth.shape[0] and truth_scoring[j] == 'U':

                    j += 1

                if j != i and j < truth.shape[0]:

                    start_mismatch[cur_truth].append((time_list[j-1] - truth_time).total_seconds())

            # Check the stop boundary

            if i > 0:

                if prediction[i-1] == truth[i-1]:

                if j != i and j < truth.shape[0]:

                    start_mismatch[cur_truth].append((time_list[j-1] - truth_time).total_seconds())

            # Check the stop boundary

            if i > 0:

                if prediction[i-1] == truth[i-1]:

                    # Previous prediction is correct, then it can only be overfill of previous truth.

                    # If there is no overfill, the stop boundary is accurate

                    j = i

                    while prediction_scoring[j] == 'o':

                        j += 1

                    stop_mismatch[prev_truth].append((time_list[j-1] - truth_time).total_seconds())

                else:

                    # Check Underfill for prev_truth (at the stop boundary)

                    j = i - 1

                    while j >= 0 and truth_scoring[j] == 'u':

                        j -= 1

                    if j != i - 1:

                        stop_mismatch[prev_truth].append((time_list[j + 1] - truth_time).total_seconds())

            if prev_truth != -1:

                if len(stop_mismatch[prev_truth]) > 0 and abs(stop_mismatch[prev_truth][-1]) > 1800:

                    logger.warning('Stop mismatch is over half an hour: %s at %d (%s) - %f' %